ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model

建筑 计算机科学 人工智能 卷积神经网络 编码器 变更检测 变压器 水准点(测量) 机器学习 地理 大地测量学 电压 视觉艺术 艺术 物理 操作系统 量子力学
作者
Hongruixuan Chen,Jian Song,Chengxi Han,Junshi Xia,Naoto Yokoya
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:62: 1-20 被引量:48
标识
DOI:10.1109/tgrs.2024.3417253
摘要

Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD).However, both architectures have inherent shortcomings: CNN are constrained by a limited receptive field that may hinder their ability to capture broader spatial contexts, while Transformers are computationally intensive, making them costly to train and deploy on large datasets.Recently, the Mamba architecture, based on state space models, has shown remarkable performance in a series of natural language processing tasks, which can effectively compensate for the shortcomings of the above two architectures.In this paper, we explore for the first time the potential of the Mamba architecture for remote sensing CD tasks.We tailor the corresponding frameworks, called MambaBCD, MambaSCD, and MambaBDA, for binary change detection (BCD), semantic change detection (SCD), and building damage assessment (BDA), respectively.All three frameworks adopt the cutting-edge Visual Mamba architecture as the encoder, which allows full learning of global spatial contextual information from the input images.For the change decoder, which is available in all three architectures, we propose three spatio-temporal relationship modeling mechanisms, which can be naturally combined with the Mamba architecture and fully utilize its attribute to achieve spatio-temporal interaction of multi-temporal features, thereby obtaining accurate change information.On five benchmark datasets, our proposed frameworks outperform current CNN-and Transformer-based approaches without using any complex training strategies or tricks, fully demonstrating the potential of the Mamba architecture in CD tasks.Further experiments show that our architecture is quite robust to degraded data.The source code is available in https://github.com/ChenHongruixuan/MambaCD.
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